Overview

Dataset statistics

Number of variables52
Number of observations37472
Missing cells55496
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 MiB
Average record size in memory264.1 B

Variable types

Categorical23
Numeric26
DateTime3

Alerts

age is highly overall correlated with lab_urea and 1 other fieldsHigh correlation
delta_days_death is highly overall correlated with hospital_outcome and 1 other fieldsHigh correlation
hospital_outcome is highly overall correlated with delta_days_deathHigh correlation
inpatient_days is highly overall correlated with delta_days_deathHigh correlation
lab_alt is highly overall correlated with lab_astHigh correlation
lab_ast is highly overall correlated with lab_alt and 1 other fieldsHigh correlation
lab_creatinine is highly overall correlated with lab_urea and 1 other fieldsHigh correlation
lab_hct is highly overall correlated with lab_hemoglobin and 1 other fieldsHigh correlation
lab_hemoglobin is highly overall correlated with lab_hct and 1 other fieldsHigh correlation
lab_ldh is highly overall correlated with lab_astHigh correlation
lab_leukocyte is highly overall correlated with lab_lymphocyte_percentage and 1 other fieldsHigh correlation
lab_lymphocyte is highly overall correlated with lab_lymphocyte_percentage and 1 other fieldsHigh correlation
lab_lymphocyte_percentage is highly overall correlated with lab_leukocyte and 3 other fieldsHigh correlation
lab_mch is highly overall correlated with lab_mcvHigh correlation
lab_mcv is highly overall correlated with lab_mchHigh correlation
lab_neutrophil is highly overall correlated with lab_leukocyte and 2 other fieldsHigh correlation
lab_neutrophil_percentage is highly overall correlated with lab_lymphocyte and 2 other fieldsHigh correlation
lab_rbc is highly overall correlated with lab_hct and 1 other fieldsHigh correlation
lab_urea is highly overall correlated with age and 1 other fieldsHigh correlation
num_shots is highly overall correlated with vaccinated and 2 other fieldsHigh correlation
pmhx_ckd is highly overall correlated with lab_creatinineHigh correlation
pmhx_htn is highly overall correlated with ageHigh correlation
vaccinated is highly overall correlated with num_shots and 2 other fieldsHigh correlation
wave_3 is highly overall correlated with num_shots and 1 other fieldsHigh correlation
wave_7 is highly overall correlated with num_shots and 1 other fieldsHigh correlation
icu is highly imbalanced (54.3%)Imbalance
pmhx_chronicliver is highly imbalanced (55.2%)Imbalance
pmhx_stroke is highly imbalanced (69.9%)Imbalance
death_datetime has 27748 (74.0%) missing valuesMissing
delta_days_death has 27748 (74.0%) missing valuesMissing
lab_alt is highly skewed (γ1 = 22.82635265)Skewed
lab_ast is highly skewed (γ1 = 30.02483039)Skewed
lab_leukocyte is highly skewed (γ1 = 45.09689427)Skewed
lab_lymphocyte is highly skewed (γ1 = 61.95799844)Skewed
num_shots has 22863 (61.0%) zerosZeros

Reproduction

Analysis started2024-01-16 08:21:15.954832
Analysis finished2024-01-16 08:24:08.409827
Duration2 minutes and 52.45 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
21552 
1
15920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

Length

2024-01-16T09:24:08.584559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:08.872308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

Most occurring characters

ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21552
57.5%
1 15920
42.5%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.090334
Minimum18
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:09.126834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35
Q154
median68
Q380
95-th percentile91
Maximum108
Range90
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.430654
Coefficient of variation (CV)0.26373983
Kurtosis-0.57663514
Mean66.090334
Median Absolute Deviation (MAD)13
Skewness-0.405563
Sum2476537
Variance303.82769
MonotonicityNot monotonic
2024-01-16T09:24:09.423672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 829
 
2.2%
75 826
 
2.2%
77 825
 
2.2%
76 824
 
2.2%
85 818
 
2.2%
86 792
 
2.1%
84 761
 
2.0%
73 758
 
2.0%
87 757
 
2.0%
88 754
 
2.0%
Other values (79) 29528
78.8%
ValueCountFrequency (%)
18 49
0.1%
19 36
 
0.1%
20 48
0.1%
21 56
0.1%
22 55
0.1%
23 77
0.2%
24 74
0.2%
25 93
0.2%
26 116
0.3%
27 114
0.3%
ValueCountFrequency (%)
108 1
 
< 0.1%
105 2
 
< 0.1%
104 2
 
< 0.1%
103 4
 
< 0.1%
102 5
 
< 0.1%
101 14
 
< 0.1%
100 29
 
0.1%
99 31
 
0.1%
98 79
0.2%
97 92
0.2%

num_shots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96338599
Minimum0
Maximum5
Zeros22863
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:09.661282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2939069
Coefficient of variation (CV)1.3430826
Kurtosis-1.0151536
Mean0.96338599
Median Absolute Deviation (MAD)0
Skewness0.80101994
Sum36100
Variance1.6741951
MonotonicityNot monotonic
2024-01-16T09:24:09.887148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22863
61.0%
3 7322
 
19.5%
2 5024
 
13.4%
1 1667
 
4.4%
4 561
 
1.5%
5 35
 
0.1%
ValueCountFrequency (%)
0 22863
61.0%
1 1667
 
4.4%
2 5024
 
13.4%
3 7322
 
19.5%
4 561
 
1.5%
5 35
 
0.1%
ValueCountFrequency (%)
5 35
 
0.1%
4 561
 
1.5%
3 7322
 
19.5%
2 5024
 
13.4%
1 1667
 
4.4%
0 22863
61.0%

type_center
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
Regional Hospital
14969 
Specialized Hospital
14035 
County Hospital
8468 

Length

Max length20
Median length17
Mean length17.671675
Min length15

Characters and Unicode

Total characters662193
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCounty Hospital
2nd rowCounty Hospital
3rd rowCounty Hospital
4th rowCounty Hospital
5th rowCounty Hospital

Common Values

ValueCountFrequency (%)
Regional Hospital 14969
39.9%
Specialized Hospital 14035
37.5%
County Hospital 8468
22.6%

Length

2024-01-16T09:24:10.144222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:10.382828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
hospital 37472
50.0%
regional 14969
 
20.0%
specialized 14035
 
18.7%
county 8468
 
11.3%

Most occurring characters

ValueCountFrequency (%)
i 80511
12.2%
a 66476
10.0%
l 66476
10.0%
o 60909
9.2%
p 51507
 
7.8%
t 45940
 
6.9%
e 43039
 
6.5%
s 37472
 
5.7%
37472
 
5.7%
H 37472
 
5.7%
Other values (10) 134919
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 549777
83.0%
Uppercase Letter 74944
 
11.3%
Space Separator 37472
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 80511
14.6%
a 66476
12.1%
l 66476
12.1%
o 60909
11.1%
p 51507
9.4%
t 45940
8.4%
e 43039
7.8%
s 37472
6.8%
n 23437
 
4.3%
g 14969
 
2.7%
Other values (5) 59041
10.7%
Uppercase Letter
ValueCountFrequency (%)
H 37472
50.0%
R 14969
 
20.0%
S 14035
 
18.7%
C 8468
 
11.3%
Space Separator
ValueCountFrequency (%)
37472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 624721
94.3%
Common 37472
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 80511
12.9%
a 66476
10.6%
l 66476
10.6%
o 60909
9.7%
p 51507
8.2%
t 45940
7.4%
e 43039
 
6.9%
s 37472
 
6.0%
H 37472
 
6.0%
n 23437
 
3.8%
Other values (9) 111482
17.8%
Common
ValueCountFrequency (%)
37472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 662193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 80511
12.2%
a 66476
10.0%
l 66476
10.0%
o 60909
9.2%
p 51507
 
7.8%
t 45940
 
6.9%
e 43039
 
6.5%
s 37472
 
5.7%
37472
 
5.7%
H 37472
 
5.7%
Other values (10) 134919
20.4%

vaccinated
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
22863 
1
14609 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

Length

2024-01-16T09:24:10.661043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:10.890942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

Most occurring characters

ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22863
61.0%
1 14609
39.0%

icu
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
33871 
1
3601 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

Length

2024-01-16T09:24:11.126448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:11.349253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33871
90.4%
1 3601
 
9.6%

inpatient_days
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.3576
Minimum0
Maximum404
Zeros130
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:11.615458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q313
95-th percentile32
Maximum404
Range404
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.912468
Coefficient of variation (CV)1.136901
Kurtosis78.299992
Mean11.3576
Median Absolute Deviation (MAD)4
Skewness6.0260633
Sum425592
Variance166.73182
MonotonicityNot monotonic
2024-01-16T09:24:11.959903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 3341
 
8.9%
5 3305
 
8.8%
4 3181
 
8.5%
7 3065
 
8.2%
8 2615
 
7.0%
3 2550
 
6.8%
9 2163
 
5.8%
10 1829
 
4.9%
11 1613
 
4.3%
2 1578
 
4.2%
Other values (149) 12232
32.6%
ValueCountFrequency (%)
0 130
 
0.3%
1 793
 
2.1%
2 1578
4.2%
3 2550
6.8%
4 3181
8.5%
5 3305
8.8%
6 3341
8.9%
7 3065
8.2%
8 2615
7.0%
9 2163
5.8%
ValueCountFrequency (%)
404 1
< 0.1%
309 1
< 0.1%
276 1
< 0.1%
273 1
< 0.1%
250 1
< 0.1%
244 1
< 0.1%
225 1
< 0.1%
212 1
< 0.1%
204 1
< 0.1%
203 2
< 0.1%
Distinct703
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size585.5 KiB
Minimum2020-12-21 00:00:00
Maximum2022-11-28 00:00:00
2024-01-16T09:24:12.286318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:12.849951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct705
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size585.5 KiB
Minimum2020-12-22 00:00:00
Maximum2022-11-30 00:00:00
2024-01-16T09:24:13.159618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:13.483106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hospital_outcome
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
30252 
1
7220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

Length

2024-01-16T09:24:13.787015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:14.015454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

Most occurring characters

ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30252
80.7%
1 7220
 
19.3%

death_datetime
Date

MISSING 

Distinct783
Distinct (%)8.1%
Missing27748
Missing (%)74.0%
Memory size585.5 KiB
Minimum2020-12-22 00:00:00
Maximum2023-02-23 00:00:00
2024-01-16T09:24:14.275225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:14.595267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

delta_days_death
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct626
Distinct (%)6.4%
Missing27748
Missing (%)74.0%
Infinite0
Infinite (%)0.0%
Mean68.127931
Minimum0
Maximum772
Zeros62
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size622.1 KiB
2024-01-16T09:24:14.932377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median16
Q353.25
95-th percentile360.85
Maximum772
Range772
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation126.43216
Coefficient of variation (CV)1.8558051
Kurtosis8.616077
Mean68.127931
Median Absolute Deviation (MAD)12
Skewness2.8880705
Sum662476
Variance15985.091
MonotonicityNot monotonic
2024-01-16T09:24:15.293032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 426
 
1.1%
3 425
 
1.1%
6 387
 
1.0%
5 362
 
1.0%
2 356
 
1.0%
9 348
 
0.9%
8 331
 
0.9%
7 323
 
0.9%
1 315
 
0.8%
10 299
 
0.8%
Other values (616) 6152
 
16.4%
(Missing) 27748
74.0%
ValueCountFrequency (%)
0 62
 
0.2%
1 315
0.8%
2 356
1.0%
3 425
1.1%
4 426
1.1%
5 362
1.0%
6 387
1.0%
7 323
0.9%
8 331
0.9%
9 348
0.9%
ValueCountFrequency (%)
772 1
< 0.1%
749 1
< 0.1%
748 1
< 0.1%
742 1
< 0.1%
739 1
< 0.1%
737 2
< 0.1%
735 2
< 0.1%
734 1
< 0.1%
732 1
< 0.1%
730 2
< 0.1%

wave_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
25530 
1
11942 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

Length

2024-01-16T09:24:15.603724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:15.837909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

Most occurring characters

ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25530
68.1%
1 11942
31.9%

wave_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
30375 
1
7097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

Length

2024-01-16T09:24:16.091200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:16.315773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

Most occurring characters

ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30375
81.1%
1 7097
 
18.9%

wave_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
32357 
1
5115 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

Length

2024-01-16T09:24:16.553652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:16.795827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32357
86.3%
1 5115
 
13.7%

wave_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
30469 
1
7003 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

Length

2024-01-16T09:24:17.039365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:17.287342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30469
81.3%
1 7003
 
18.7%

wave_7
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
31157 
1
6315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%

Length

2024-01-16T09:24:17.541831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:17.766094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31157
83.1%
1 6315
 
16.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
32021 
1
5451 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

Length

2024-01-16T09:24:17.997838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:18.218308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32021
85.5%
1 5451
 
14.5%

pmhx_asthma
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
32443 
1
5029 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

Length

2024-01-16T09:24:18.454509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:18.696958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32443
86.6%
1 5029
 
13.4%

pmhx_chf
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
28744 
1
8728 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

Length

2024-01-16T09:24:18.960032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:19.188273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28744
76.7%
1 8728
 
23.3%

pmhx_chronicliver
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
33971 
1
3501 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

Length

2024-01-16T09:24:19.430600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:19.657511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33971
90.7%
1 3501
 
9.3%

pmhx_ckd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
31069 
1
6403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

Length

2024-01-16T09:24:19.893288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:20.108913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31069
82.9%
1 6403
 
17.1%

pmhx_copd
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
29487 
1
7985 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

Length

2024-01-16T09:24:20.347141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:20.567250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29487
78.7%
1 7985
 
21.3%

pmhx_dementia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
30879 
1
6593 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

Length

2024-01-16T09:24:20.813809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:21.043347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30879
82.4%
1 6593
 
17.6%

pmhx_diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
24076 
1
13396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

Length

2024-01-16T09:24:21.283305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:21.509202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

Most occurring characters

ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24076
64.3%
1 13396
35.7%

pmhx_hld
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
1
20414 
0
17058 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

Length

2024-01-16T09:24:21.760464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:21.978762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

Most occurring characters

ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20414
54.5%
0 17058
45.5%

pmhx_htn
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
1
25096 
0
12376 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

Length

2024-01-16T09:24:22.211995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:22.435881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

Most occurring characters

ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25096
67.0%
0 12376
33.0%

pmhx_ihd
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
32859 
1
4613 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

Length

2024-01-16T09:24:22.668948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:22.900501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32859
87.7%
1 4613
 
12.3%

pmhx_obesity
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
28035 
1
9437 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

Length

2024-01-16T09:24:23.139795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:23.357673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

Most occurring characters

ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28035
74.8%
1 9437
 
25.2%

pmhx_stroke
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.5 KiB
0
35469 
1
 
2003

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

Length

2024-01-16T09:24:23.605134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T09:24:24.080207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35469
94.7%
1 2003
 
5.3%

lab_alt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3915
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.7487
Minimum0
Maximum4308
Zeros49
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:24.342636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q119
median31
Q343
95-th percentile95.4895
Maximum4308
Range4308
Interquartile range (IQR)24

Descriptive statistics

Standard deviation54.732262
Coefficient of variation (CV)1.3769573
Kurtosis1226.6947
Mean39.7487
Median Absolute Deviation (MAD)12
Skewness22.826353
Sum1489463.3
Variance2995.6205
MonotonicityNot monotonic
2024-01-16T09:24:24.663666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1364
 
3.6%
21 1276
 
3.4%
14 1153
 
3.1%
15 1070
 
2.9%
11 1048
 
2.8%
25 959
 
2.6%
28 889
 
2.4%
17 883
 
2.4%
24 835
 
2.2%
31 806
 
2.2%
Other values (3905) 27189
72.6%
ValueCountFrequency (%)
0 49
0.1%
0.1 1
 
< 0.1%
0.35 1
 
< 0.1%
0.85 1
 
< 0.1%
0.88 1
 
< 0.1%
0.98 1
 
< 0.1%
1 16
 
< 0.1%
1.1 1
 
< 0.1%
1.6 1
 
< 0.1%
1.84 1
 
< 0.1%
ValueCountFrequency (%)
4308 1
< 0.1%
2188 1
< 0.1%
1993.65 1
< 0.1%
1685 1
< 0.1%
1643 1
< 0.1%
1641 1
< 0.1%
1368 1
< 0.1%
1251 1
< 0.1%
1208 1
< 0.1%
1196 1
< 0.1%

lab_ast
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5465
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.670894
Minimum0
Maximum4982
Zeros212
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:24.984942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q125
median37.33
Q350
95-th percentile102
Maximum4982
Range4982
Interquartile range (IQR)25

Descriptive statistics

Standard deviation76.356505
Coefficient of variation (CV)1.6360626
Kurtosis1449.4775
Mean46.670894
Median Absolute Deviation (MAD)12.33
Skewness30.02483
Sum1748851.7
Variance5830.3158
MonotonicityNot monotonic
2024-01-16T09:24:25.317755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1023
 
2.7%
21 1006
 
2.7%
28 1004
 
2.7%
31 970
 
2.6%
24 785
 
2.1%
18 783
 
2.1%
34 756
 
2.0%
38 723
 
1.9%
22 710
 
1.9%
27 700
 
1.9%
Other values (5455) 29012
77.4%
ValueCountFrequency (%)
0 212
0.6%
0.08 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.14 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 3
 
< 0.1%
0.21 3
 
< 0.1%
0.22 1
 
< 0.1%
0.31 1
 
< 0.1%
ValueCountFrequency (%)
4982 1
< 0.1%
4854 1
< 0.1%
4212 1
< 0.1%
3101 1
< 0.1%
2862 1
< 0.1%
2770 1
< 0.1%
2673 1
< 0.1%
2240.07 1
< 0.1%
2180 1
< 0.1%
2157 1
< 0.1%

lab_creatinine
Real number (ℝ)

HIGH CORRELATION 

Distinct745
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2014515
Minimum0.09
Maximum16.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:25.601606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.59
Q10.79
median0.99
Q31.25
95-th percentile2.5
Maximum16.5
Range16.41
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.88370062
Coefficient of variation (CV)0.73552751
Kurtosis45.353988
Mean1.2014515
Median Absolute Deviation (MAD)0.23
Skewness5.4190003
Sum45020.79
Variance0.78092678
MonotonicityNot monotonic
2024-01-16T09:24:25.940987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.19 764
 
2.0%
0.9 724
 
1.9%
1.2 682
 
1.8%
0.84 589
 
1.6%
0.73 575
 
1.5%
0.8 551
 
1.5%
0.78 548
 
1.5%
0.82 543
 
1.4%
0.99 542
 
1.4%
0.81 541
 
1.4%
Other values (735) 31413
83.8%
ValueCountFrequency (%)
0.09 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.2 1
 
< 0.1%
0.21 4
< 0.1%
0.23 3
< 0.1%
0.24 3
< 0.1%
0.25 2
 
< 0.1%
0.26 3
< 0.1%
0.27 6
< 0.1%
ValueCountFrequency (%)
16.5 1
< 0.1%
14.55 1
< 0.1%
14.22 1
< 0.1%
13.42 1
< 0.1%
13.3 1
< 0.1%
13.13 1
< 0.1%
13.08 1
< 0.1%
13.06 1
< 0.1%
13.05 1
< 0.1%
13.02 1
< 0.1%

lab_crp
Real number (ℝ)

Distinct5624
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.76228
Minimum0
Maximum644.7
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:26.246354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.5
Q140.1
median89.3
Q3134.3
95-th percentile262.59
Maximum644.7
Range644.7
Interquartile range (IQR)94.2

Descriptive statistics

Standard deviation80.248396
Coefficient of variation (CV)0.79641305
Kurtosis2.9059477
Mean100.76228
Median Absolute Deviation (MAD)47.7
Skewness1.4384892
Sum3775764.2
Variance6439.805
MonotonicityNot monotonic
2024-01-16T09:24:26.536196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.85 143
 
0.4%
101.86 95
 
0.3%
101.84 75
 
0.2%
101.87 60
 
0.2%
6 42
 
0.1%
10.5 41
 
0.1%
103 40
 
0.1%
10.6 40
 
0.1%
101.92 40
 
0.1%
16 39
 
0.1%
Other values (5614) 36857
98.4%
ValueCountFrequency (%)
0 9
 
< 0.1%
0.2 10
< 0.1%
0.3 5
 
< 0.1%
0.4 8
 
< 0.1%
0.5 10
< 0.1%
0.6 16
< 0.1%
0.7 15
< 0.1%
0.8 17
< 0.1%
0.9 12
< 0.1%
1 24
0.1%
ValueCountFrequency (%)
644.7 1
< 0.1%
642.3 1
< 0.1%
636.2 1
< 0.1%
629.1 1
< 0.1%
614.3 1
< 0.1%
610.9 1
< 0.1%
609.1 1
< 0.1%
606.5 1
< 0.1%
597 1
< 0.1%
588.7 1
< 0.1%

lab_ddimer
Real number (ℝ)

Distinct13382
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2335.1516
Minimum0
Maximum368520
Zeros231
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:26.829735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile270
Q1561
median1087.5
Q32272.45
95-th percentile6098.5355
Maximum368520
Range368520
Interquartile range (IQR)1711.45

Descriptive statistics

Standard deviation6882.1317
Coefficient of variation (CV)2.9471884
Kurtosis516.49849
Mean2335.1516
Median Absolute Deviation (MAD)662.245
Skewness17.04504
Sum87502800
Variance47363736
MonotonicityNot monotonic
2024-01-16T09:24:27.147569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 231
 
0.6%
500 144
 
0.4%
400 132
 
0.4%
600 116
 
0.3%
380 114
 
0.3%
460 113
 
0.3%
490 112
 
0.3%
330 112
 
0.3%
420 112
 
0.3%
520 111
 
0.3%
Other values (13372) 36175
96.5%
ValueCountFrequency (%)
0 231
0.6%
1 1
 
< 0.1%
7 1
 
< 0.1%
8.56 1
 
< 0.1%
9.9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
12.32 1
 
< 0.1%
13.69 1
 
< 0.1%
14.67 1
 
< 0.1%
ValueCountFrequency (%)
368520 1
< 0.1%
316200 1
< 0.1%
257210 1
< 0.1%
216000 1
< 0.1%
157100 1
< 0.1%
148597 1
< 0.1%
147500 1
< 0.1%
138093 1
< 0.1%
136512 1
< 0.1%
130093 1
< 0.1%

lab_glucose
Real number (ℝ)

Distinct2102
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.85804
Minimum5
Maximum1156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:27.446507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile86
Q1103
median123
Q3149
95-th percentile267
Maximum1156
Range1151
Interquartile range (IQR)46

Descriptive statistics

Standard deviation66.831828
Coefficient of variation (CV)0.4744623
Kurtosis23.362109
Mean140.85804
Median Absolute Deviation (MAD)21
Skewness3.6780449
Sum5278232.3
Variance4466.4932
MonotonicityNot monotonic
2024-01-16T09:24:27.770873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 872
 
2.3%
101 851
 
2.3%
107 843
 
2.2%
105 813
 
2.2%
98 769
 
2.1%
112 767
 
2.0%
114 761
 
2.0%
96 755
 
2.0%
109 746
 
2.0%
94 726
 
1.9%
Other values (2092) 29569
78.9%
ValueCountFrequency (%)
5 1
 
< 0.1%
7 1
 
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
21 1
 
< 0.1%
23 1
 
< 0.1%
25 4
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
29 2
< 0.1%
ValueCountFrequency (%)
1156 1
< 0.1%
1126 1
< 0.1%
1121 1
< 0.1%
1076 1
< 0.1%
1007 1
< 0.1%
983 1
< 0.1%
969 1
< 0.1%
952 1
< 0.1%
901 1
< 0.1%
867 1
< 0.1%

lab_hct
Real number (ℝ)

HIGH CORRELATION 

Distinct883
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.790573
Minimum0
Maximum71
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:28.077144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.655
Q137
median40.4
Q344.2
95-th percentile49.2
Maximum71
Range71
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation10.148214
Coefficient of variation (CV)0.26161547
Kurtosis6.9947347
Mean38.790573
Median Absolute Deviation (MAD)3.7
Skewness-2.4315329
Sum1453560.4
Variance102.98625
MonotonicityNot monotonic
2024-01-16T09:24:28.394145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 892
 
2.4%
0.3 704
 
1.9%
39 582
 
1.6%
43 474
 
1.3%
44 455
 
1.2%
41 454
 
1.2%
42 451
 
1.2%
40 417
 
1.1%
45 353
 
0.9%
38 345
 
0.9%
Other values (873) 32345
86.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 86
 
0.2%
0.3 704
1.9%
0.4 892
2.4%
0.5 65
 
0.2%
9.5 1
 
< 0.1%
9.67 1
 
< 0.1%
10.1 1
 
< 0.1%
10.7 1
 
< 0.1%
ValueCountFrequency (%)
71 1
 
< 0.1%
68.3 1
 
< 0.1%
66.28 1
 
< 0.1%
65.2 1
 
< 0.1%
64.5 1
 
< 0.1%
63.2 1
 
< 0.1%
61.9 1
 
< 0.1%
61.7 2
< 0.1%
61.6 1
 
< 0.1%
61.1 4
< 0.1%

lab_hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct423
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.501137
Minimum2.4
Maximum23.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:28.704715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile9.8
Q112.4
median13.55
Q314.9
95-th percentile16.5
Maximum23.4
Range21
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.0324013
Coefficient of variation (CV)0.15053557
Kurtosis0.80200754
Mean13.501137
Median Absolute Deviation (MAD)1.25
Skewness-0.51420113
Sum505914.61
Variance4.1306552
MonotonicityNot monotonic
2024-01-16T09:24:29.008255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.54 1444
 
3.9%
14 727
 
1.9%
14.1 717
 
1.9%
13.7 710
 
1.9%
14.3 702
 
1.9%
13.4 700
 
1.9%
14.5 690
 
1.8%
13.5 681
 
1.8%
13.6 669
 
1.8%
14.7 666
 
1.8%
Other values (413) 29766
79.4%
ValueCountFrequency (%)
2.4 1
< 0.1%
2.6 2
< 0.1%
3.2 1
< 0.1%
3.33 1
< 0.1%
3.8 2
< 0.1%
3.9 2
< 0.1%
4 1
< 0.1%
4.3 2
< 0.1%
4.4 1
< 0.1%
4.5 1
< 0.1%
ValueCountFrequency (%)
23.4 1
< 0.1%
22.3 1
< 0.1%
21.9 1
< 0.1%
21.7 1
< 0.1%
21.1 1
< 0.1%
21 1
< 0.1%
20.9 1
< 0.1%
20.5 1
< 0.1%
20.3 1
< 0.1%
20.2 1
< 0.1%

lab_inr
Real number (ℝ)

Distinct594
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2488164
Minimum0.31
Maximum22.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:29.292764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.31
5-th percentile0.92
Q11.02
median1.12
Q31.25
95-th percentile1.7345
Maximum22.81
Range22.5
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.70000604
Coefficient of variation (CV)0.56053557
Kurtosis152.55528
Mean1.2488164
Median Absolute Deviation (MAD)0.11
Skewness10.114185
Sum46795.65
Variance0.49000846
MonotonicityNot monotonic
2024-01-16T09:24:29.581404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.03 1312
 
3.5%
1.25 1147
 
3.1%
1.05 977
 
2.6%
1.04 975
 
2.6%
1.02 973
 
2.6%
1.08 973
 
2.6%
1.09 972
 
2.6%
1.1 961
 
2.6%
0.97 950
 
2.5%
1.06 900
 
2.4%
Other values (584) 27332
72.9%
ValueCountFrequency (%)
0.31 1
 
< 0.1%
0.6 1
 
< 0.1%
0.73 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 2
 
< 0.1%
0.78 1
 
< 0.1%
0.79 2
 
< 0.1%
0.8 76
0.2%
0.81 15
 
< 0.1%
0.82 27
 
0.1%
ValueCountFrequency (%)
22.81 1
< 0.1%
19.14 1
< 0.1%
17.86 1
< 0.1%
17.42 1
< 0.1%
15.9 1
< 0.1%
15.82 1
< 0.1%
15.41 1
< 0.1%
15.15 1
< 0.1%
14.06 1
< 0.1%
13.79 1
< 0.1%

lab_ldh
Real number (ℝ)

HIGH CORRELATION 

Distinct6874
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364.54959
Minimum2
Maximum8628.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:29.888200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile183
Q1262
median338
Q3406
95-th percentile651
Maximum8628.45
Range8626.45
Interquartile range (IQR)144

Descriptive statistics

Standard deviation200.51061
Coefficient of variation (CV)0.55002288
Kurtosis241.5171
Mean364.54959
Median Absolute Deviation (MAD)73
Skewness9.5648667
Sum13660402
Variance40204.506
MonotonicityNot monotonic
2024-01-16T09:24:30.197505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 193
 
0.5%
265 190
 
0.5%
259 188
 
0.5%
286 175
 
0.5%
274 172
 
0.5%
225 170
 
0.5%
289 170
 
0.5%
231 168
 
0.4%
253 167
 
0.4%
240 166
 
0.4%
Other values (6864) 35713
95.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
4.18 1
 
< 0.1%
40.68 1
 
< 0.1%
65 1
 
< 0.1%
81 1
 
< 0.1%
86 1
 
< 0.1%
87.37 1
 
< 0.1%
91 1
 
< 0.1%
92 3
< 0.1%
94 1
 
< 0.1%
ValueCountFrequency (%)
8628.45 1
< 0.1%
7707 1
< 0.1%
7297 1
< 0.1%
6589 1
< 0.1%
4949 1
< 0.1%
4400 1
< 0.1%
4200 1
< 0.1%
4053.95 1
< 0.1%
3944 1
< 0.1%
3934 1
< 0.1%

lab_leukocyte
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2524
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3212246
Minimum0.03
Maximum938.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:30.741607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile3.38
Q15.32
median7.4
Q39.37
95-th percentile16.19
Maximum938.64
Range938.61
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation9.5877097
Coefficient of variation (CV)1.1521994
Kurtosis3341.7479
Mean8.3212246
Median Absolute Deviation (MAD)2.04
Skewness45.096894
Sum311812.93
Variance91.924177
MonotonicityNot monotonic
2024-01-16T09:24:31.047306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.23 709
 
1.9%
8.24 443
 
1.2%
8.22 341
 
0.9%
8.25 274
 
0.7%
8.21 253
 
0.7%
8.2 230
 
0.6%
8.26 137
 
0.4%
8.18 108
 
0.3%
8.27 105
 
0.3%
8.19 101
 
0.3%
Other values (2514) 34771
92.8%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.08 1
 
< 0.1%
0.14 3
< 0.1%
0.19 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 2
< 0.1%
0.24 1
 
< 0.1%
0.28 1
 
< 0.1%
0.29 1
 
< 0.1%
0.3 2
< 0.1%
ValueCountFrequency (%)
938.64 1
< 0.1%
591.02 1
< 0.1%
550.45 1
< 0.1%
495.69 1
< 0.1%
397.46 1
< 0.1%
297.09 1
< 0.1%
255.5 1
< 0.1%
242.22 1
< 0.1%
212.67 1
< 0.1%
212.45 1
< 0.1%

lab_lymphocyte
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct743
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3761104
Minimum0
Maximum787.52
Zeros120
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:31.347813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q10.69
median1.02
Q31.35
95-th percentile2.22
Maximum787.52
Range787.52
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation7.5849759
Coefficient of variation (CV)5.5118948
Kurtosis5030.0673
Mean1.3761104
Median Absolute Deviation (MAD)0.33
Skewness61.957998
Sum51565.61
Variance57.531859
MonotonicityNot monotonic
2024-01-16T09:24:31.673951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 1733
 
4.6%
1.34 668
 
1.8%
1.36 383
 
1.0%
1.33 368
 
1.0%
0.68 316
 
0.8%
0.89 314
 
0.8%
0.69 310
 
0.8%
0.84 305
 
0.8%
0.72 304
 
0.8%
0.66 303
 
0.8%
Other values (733) 32468
86.6%
ValueCountFrequency (%)
0 120
0.3%
0.01 1
 
< 0.1%
0.02 2
 
< 0.1%
0.03 3
 
< 0.1%
0.04 3
 
< 0.1%
0.05 9
 
< 0.1%
0.06 4
 
< 0.1%
0.07 10
 
< 0.1%
0.08 4
 
< 0.1%
0.09 16
 
< 0.1%
ValueCountFrequency (%)
787.52 1
< 0.1%
573.88 1
< 0.1%
494.3 1
< 0.1%
489.74 1
< 0.1%
262.9 1
< 0.1%
256.9 1
< 0.1%
204.46 1
< 0.1%
201.01 1
< 0.1%
182.46 1
< 0.1%
170.15 1
< 0.1%

lab_lymphocyte_percentage
Real number (ℝ)

HIGH CORRELATION 

Distinct2250
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.312263
Minimum0
Maximum99.7
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:32.006900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q19.6
median15.8
Q320.3
95-th percentile33.2
Maximum99.7
Range99.7
Interquartile range (IQR)10.7

Descriptive statistics

Standard deviation9.7639011
Coefficient of variation (CV)0.59856202
Kurtosis9.0840571
Mean16.312263
Median Absolute Deviation (MAD)5.43
Skewness2.0045692
Sum611253.12
Variance95.333764
MonotonicityNot monotonic
2024-01-16T09:24:32.340137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4 229
 
0.6%
16.41 204
 
0.5%
16.3 193
 
0.5%
7 172
 
0.5%
10.8 166
 
0.4%
10.2 165
 
0.4%
16.5 162
 
0.4%
11.1 162
 
0.4%
8.4 159
 
0.4%
11.4 159
 
0.4%
Other values (2240) 35701
95.3%
ValueCountFrequency (%)
0 4
< 0.1%
0.3 1
 
< 0.1%
0.38 1
 
< 0.1%
0.4 2
 
< 0.1%
0.54 1
 
< 0.1%
0.6 5
< 0.1%
0.67 1
 
< 0.1%
0.7 7
< 0.1%
0.8 7
< 0.1%
0.9 7
< 0.1%
ValueCountFrequency (%)
99.7 1
< 0.1%
99.28 1
< 0.1%
98.8 1
< 0.1%
97.1 1
< 0.1%
96.4 1
< 0.1%
96.3 1
< 0.1%
96.2 1
< 0.1%
95.8 1
< 0.1%
95.3 1
< 0.1%
94.5 1
< 0.1%

lab_mch
Real number (ℝ)

HIGH CORRELATION 

Distinct510
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.592024
Minimum13.3
Maximum93.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:32.662982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13.3
5-th percentile25.4
Q128.5
median29.62
Q330.9
95-th percentile33.1
Maximum93.9
Range80.6
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.5234256
Coefficient of variation (CV)0.085273841
Kurtosis16.702555
Mean29.592024
Median Absolute Deviation (MAD)1.22
Skewness-0.0027643684
Sum1108872.3
Variance6.3676768
MonotonicityNot monotonic
2024-01-16T09:24:33.039489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.61 1250
 
3.3%
29.6 956
 
2.6%
29.7 744
 
2.0%
30.2 707
 
1.9%
30.3 700
 
1.9%
29.9 699
 
1.9%
30 696
 
1.9%
29.8 691
 
1.8%
29.4 688
 
1.8%
30.1 687
 
1.8%
Other values (500) 29654
79.1%
ValueCountFrequency (%)
13.3 1
< 0.1%
13.7 1
< 0.1%
13.79 1
< 0.1%
14.6 1
< 0.1%
14.9 1
< 0.1%
15 2
< 0.1%
15.5 1
< 0.1%
15.6 2
< 0.1%
15.7 2
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
93.9 1
< 0.1%
62.2 1
< 0.1%
59.9 1
< 0.1%
52.4 1
< 0.1%
48 1
< 0.1%
47.5 1
< 0.1%
47.1 1
< 0.1%
47 1
< 0.1%
46.3 1
< 0.1%
46.2 1
< 0.1%

lab_mcv
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.50352
Minimum54.2
Maximum149.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:33.413938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum54.2
5-th percentile79.5
Q186.1
median89.44
Q392.9
95-th percentile100.1
Maximum149.7
Range95.5
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation6.6474846
Coefficient of variation (CV)0.07427065
Kurtosis3.9082517
Mean89.50352
Median Absolute Deviation (MAD)3.36
Skewness0.063227376
Sum3353875.9
Variance44.189052
MonotonicityNot monotonic
2024-01-16T09:24:33.764426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.4 372
 
1.0%
89.45 297
 
0.8%
89.5 292
 
0.8%
88.8 292
 
0.8%
89.2 292
 
0.8%
90.3 268
 
0.7%
90 268
 
0.7%
88.2 263
 
0.7%
89.8 262
 
0.7%
90.2 259
 
0.7%
Other values (1028) 34607
92.4%
ValueCountFrequency (%)
54.2 1
< 0.1%
54.8 1
< 0.1%
55 2
< 0.1%
55.5 1
< 0.1%
55.6 2
< 0.1%
56.5 2
< 0.1%
56.6 1
< 0.1%
57 1
< 0.1%
57.9 1
< 0.1%
58.1 1
< 0.1%
ValueCountFrequency (%)
149.7 1
< 0.1%
145.1 1
< 0.1%
139.8 1
< 0.1%
138.6 1
< 0.1%
137.4 1
< 0.1%
136.7 1
< 0.1%
135 1
< 0.1%
134.7 1
< 0.1%
134.3 1
< 0.1%
134.2 1
< 0.1%

lab_neutrophil
Real number (ℝ)

HIGH CORRELATION 

Distinct2216
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2919241
Minimum0
Maximum180.71
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:34.052501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.16
Q13.78
median5.65
Q37.46
95-th percentile13.5645
Maximum180.71
Range180.71
Interquartile range (IQR)3.68

Descriptive statistics

Standard deviation3.9558687
Coefficient of variation (CV)0.62872162
Kurtosis114.1684
Mean6.2919241
Median Absolute Deviation (MAD)1.85
Skewness4.5939158
Sum235770.98
Variance15.648897
MonotonicityNot monotonic
2024-01-16T09:24:34.350162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.24 573
 
1.5%
6.25 530
 
1.4%
6.23 314
 
0.8%
6.22 299
 
0.8%
6.26 298
 
0.8%
6.21 254
 
0.7%
6.27 155
 
0.4%
6.2 117
 
0.3%
6.16 106
 
0.3%
6.29 103
 
0.3%
Other values (2206) 34723
92.7%
ValueCountFrequency (%)
0 3
< 0.1%
0.01 1
 
< 0.1%
0.02 5
< 0.1%
0.03 5
< 0.1%
0.04 3
< 0.1%
0.06 4
< 0.1%
0.07 2
 
< 0.1%
0.09 2
 
< 0.1%
0.1 3
< 0.1%
0.11 3
< 0.1%
ValueCountFrequency (%)
180.71 1
< 0.1%
85.51 1
< 0.1%
55.36 1
< 0.1%
54.63 1
< 0.1%
51.21 1
< 0.1%
47.34 1
< 0.1%
45.51 1
< 0.1%
44.9 1
< 0.1%
43.11 1
< 0.1%
42.22 1
< 0.1%

lab_neutrophil_percentage
Real number (ℝ)

HIGH CORRELATION 

Distinct2685
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.514492
Minimum0
Maximum101.21
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:34.658702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.4
Q170.29
median75.7
Q383.5
95-th percentile91.3
Maximum101.21
Range101.21
Interquartile range (IQR)13.21

Descriptive statistics

Standard deviation11.662877
Coefficient of variation (CV)0.15444555
Kurtosis4.97109
Mean75.514492
Median Absolute Deviation (MAD)6.7
Skewness-1.4507216
Sum2829679
Variance136.02271
MonotonicityNot monotonic
2024-01-16T09:24:34.958144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.4 218
 
0.6%
75.5 213
 
0.6%
75.44 188
 
0.5%
75.43 155
 
0.4%
75.45 133
 
0.4%
75.3 131
 
0.3%
77.2 128
 
0.3%
75.2 128
 
0.3%
81.8 128
 
0.3%
86.8 125
 
0.3%
Other values (2675) 35925
95.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.3 1
 
< 0.1%
0.5 2
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 1
 
< 0.1%
1 1
 
< 0.1%
1.4 2
< 0.1%
1.7 2
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
101.21 1
 
< 0.1%
100.44 1
 
< 0.1%
98.3 2
< 0.1%
98.1 1
 
< 0.1%
97.8 3
< 0.1%
97.7 2
< 0.1%
97.5 2
< 0.1%
97.4 4
< 0.1%
97.3 2
< 0.1%
97.22 1
 
< 0.1%

lab_platelet
Real number (ℝ)

Distinct1839
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.01453
Minimum1
Maximum1094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:35.266198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile104
Q1160
median210
Q3253
95-th percentile387
Maximum1094
Range1093
Interquartile range (IQR)93

Descriptive statistics

Standard deviation90.326666
Coefficient of variation (CV)0.41431489
Kurtosis5.7383676
Mean218.01453
Median Absolute Deviation (MAD)48
Skewness1.5971008
Sum8169440.4
Variance8158.9065
MonotonicityNot monotonic
2024-01-16T09:24:35.566000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 224
 
0.6%
184 223
 
0.6%
171 214
 
0.6%
173 213
 
0.6%
177 209
 
0.6%
187 208
 
0.6%
156 206
 
0.5%
174 204
 
0.5%
175 203
 
0.5%
176 202
 
0.5%
Other values (1829) 35366
94.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 3
< 0.1%
5 4
< 0.1%
6 6
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 4
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
1094 1
< 0.1%
1091 1
< 0.1%
1027 1
< 0.1%
1001 1
< 0.1%
994 1
< 0.1%
993 1
< 0.1%
962 1
< 0.1%
959 1
< 0.1%
937 1
< 0.1%
935 1
< 0.1%

lab_potassium
Real number (ℝ)

Distinct225
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1892373
Minimum1.5
Maximum11.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:35.869375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3.3
Q13.87
median4.18
Q34.5
95-th percentile5.2
Maximum11.3
Range9.8
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.57634946
Coefficient of variation (CV)0.13757861
Kurtosis3.9517171
Mean4.1892373
Median Absolute Deviation (MAD)0.32
Skewness0.92318373
Sum156979.1
Variance0.3321787
MonotonicityNot monotonic
2024-01-16T09:24:36.169857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 2657
 
7.1%
4.1 2615
 
7.0%
4 2523
 
6.7%
3.9 2319
 
6.2%
4.3 2192
 
5.8%
3.8 2160
 
5.8%
4.4 1918
 
5.1%
3.7 1794
 
4.8%
4.5 1728
 
4.6%
3.6 1448
 
3.9%
Other values (215) 16118
43.0%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.6 1
 
< 0.1%
1.7 2
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 5
 
< 0.1%
2.2 2
 
< 0.1%
2.3 9
< 0.1%
2.4 15
< 0.1%
2.5 22
0.1%
ValueCountFrequency (%)
11.3 1
< 0.1%
9.8 1
< 0.1%
9.2 1
< 0.1%
9 1
< 0.1%
8.8 1
< 0.1%
8.6 1
< 0.1%
8.5 1
< 0.1%
8.4 1
< 0.1%
8.2 2
< 0.1%
8.1 1
< 0.1%

lab_rbc
Real number (ℝ)

HIGH CORRELATION 

Distinct552
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.583228
Minimum0.66
Maximum8.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:36.453863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile3.33
Q14.2
median4.6
Q35.02
95-th percentile5.63
Maximum8.42
Range7.76
Interquartile range (IQR)0.82

Descriptive statistics

Standard deviation0.69580427
Coefficient of variation (CV)0.15181533
Kurtosis1.1555351
Mean4.583228
Median Absolute Deviation (MAD)0.4
Skewness-0.43136388
Sum171742.72
Variance0.48414358
MonotonicityNot monotonic
2024-01-16T09:24:37.046319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.59 2082
 
5.6%
4.6 947
 
2.5%
4.8 585
 
1.6%
4.7 560
 
1.5%
4.5 519
 
1.4%
5 516
 
1.4%
4.9 476
 
1.3%
4.4 474
 
1.3%
5.1 418
 
1.1%
4.2 417
 
1.1%
Other values (542) 30478
81.3%
ValueCountFrequency (%)
0.66 1
< 0.1%
0.82 1
< 0.1%
0.93 1
< 0.1%
1 1
< 0.1%
1.06 1
< 0.1%
1.09 1
< 0.1%
1.2 1
< 0.1%
1.21 1
< 0.1%
1.25 1
< 0.1%
1.27 1
< 0.1%
ValueCountFrequency (%)
8.42 1
< 0.1%
8.4 1
< 0.1%
8.18 1
< 0.1%
8.03 1
< 0.1%
7.75 1
< 0.1%
7.7 1
< 0.1%
7.6 1
< 0.1%
7.56 1
< 0.1%
7.54 1
< 0.1%
7.48 1
< 0.1%

lab_sodium
Real number (ℝ)

Distinct645
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.94024
Minimum67
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:37.359457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile130
Q1135
median137
Q3139
95-th percentile143
Maximum187
Range120
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7378606
Coefficient of variation (CV)0.034598015
Kurtosis10.403622
Mean136.94024
Median Absolute Deviation (MAD)2
Skewness0.66327353
Sum5131424.9
Variance22.447323
MonotonicityNot monotonic
2024-01-16T09:24:37.659832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 3801
 
10.1%
138 3455
 
9.2%
136 3362
 
9.0%
139 3147
 
8.4%
135 2946
 
7.9%
140 2514
 
6.7%
134 2393
 
6.4%
133 1812
 
4.8%
141 1772
 
4.7%
132 1355
 
3.6%
Other values (635) 10915
29.1%
ValueCountFrequency (%)
67 1
 
< 0.1%
99 1
 
< 0.1%
100 1
 
< 0.1%
102 1
 
< 0.1%
104 2
 
< 0.1%
105 1
 
< 0.1%
106 2
 
< 0.1%
107 1
 
< 0.1%
108 2
 
< 0.1%
109 7
< 0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
181 1
 
< 0.1%
179 1
 
< 0.1%
178 2
 
< 0.1%
177 5
< 0.1%
176 1
 
< 0.1%
175 2
 
< 0.1%
174 1
 
< 0.1%
173 2
 
< 0.1%
172 5
< 0.1%

lab_urea
Real number (ℝ)

HIGH CORRELATION 

Distinct2897
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.543614
Minimum0
Maximum540
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size585.5 KiB
2024-01-16T09:24:37.954015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q130
median41
Q358
95-th percentile124
Maximum540
Range540
Interquartile range (IQR)28

Descriptive statistics

Standard deviation37.679068
Coefficient of variation (CV)0.73101331
Kurtosis15.074164
Mean51.543614
Median Absolute Deviation (MAD)13
Skewness3.0840045
Sum1931442.3
Variance1419.7121
MonotonicityNot monotonic
2024-01-16T09:24:38.263095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 1223
 
3.3%
31 1158
 
3.1%
35 1078
 
2.9%
25 966
 
2.6%
38 914
 
2.4%
21 865
 
2.3%
41 862
 
2.3%
24 844
 
2.3%
27 829
 
2.2%
30 818
 
2.2%
Other values (2887) 27915
74.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
1.18 1
 
< 0.1%
2.54 1
 
< 0.1%
3.71 1
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 7
< 0.1%
7.93 1
 
< 0.1%
8 15
< 0.1%
9 10
< 0.1%
ValueCountFrequency (%)
540 1
< 0.1%
511 1
< 0.1%
504 1
< 0.1%
487 1
< 0.1%
478 1
< 0.1%
464 1
< 0.1%
447 1
< 0.1%
424.71 1
< 0.1%
408 1
< 0.1%
395 1
< 0.1%

Interactions

2024-01-16T09:24:00.584317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:37.412247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:42.795958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:48.523805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:54.031042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:59.931949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:05.376967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:11.400464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:17.166441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:22.892002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:28.762965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:34.567680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:40.176754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:45.961307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:51.601652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:57.568118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:03.112293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:09.375886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:14.842726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:20.757379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:25.950874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:32.071718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:37.610348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:43.282293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:48.682546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:54.930702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:00.780412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:37.676897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:42.999336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:48.730280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:54.233505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:00.107593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:05.575431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:11.601682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:17.380973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:23.099550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:28.968171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:34.757646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:40.376314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:46.170650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:51.795257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:57.759193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:03.321956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:09.578394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:15.059005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:20.938639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:26.149249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:32.266709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:37.815022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:43.476798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:48.926995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:55.123417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:00.988526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:37.881393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:43.243060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:48.941297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:54.446627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:00.286512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:05.785016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:11.818858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:17.590964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:23.326500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:29.184350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:34.966443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:40.571857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:46.379215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:52.010388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:57.959829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:03.535995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:09.777814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:15.284107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:21.136803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:26.360149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:32.477677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:38.018427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:43.686577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:49.162212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:55.320686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:01.234532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:38.103836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:43.466363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:49.168480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:54.675106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:00.493167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:06.044061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:12.054145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:17.832675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:23.580681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:29.414700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:35.209621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:40.803987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:46.627916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:52.250141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:58.190800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:03.779178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:10.003703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:15.523855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:21.346449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:26.629156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:32.693530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:38.228734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:43.913177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:49.386430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:55.549243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:01.465794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:38.320646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:43.701660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:49.396552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:54.918381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:00.716932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:06.314274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:12.291441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:18.079407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:23.827872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:29.640337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:35.433819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:41.062809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:46.871828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:52.476033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:58.420306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:04.011265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:10.231109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:15.769299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:21.561022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:26.874600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:32.921659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:38.444133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:44.180071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:49.611093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:55.799735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:01.653945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:38.500622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:43.888708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:49.600533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2024-01-16T09:22:56.316568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:01.854379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:08.022510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:13.606814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:19.521486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:24.721733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:30.710109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:36.321079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:42.051064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:47.365283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:53.667013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:59.260656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:05.363904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:41.765933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:47.485496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:52.936423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:58.890514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:04.308657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:10.317507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:16.014501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:21.896259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:27.648185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:33.490502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:39.027707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:44.963514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:50.535727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:56.520593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:02.086660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:08.243148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:13.808161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:19.723452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:24.925984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:30.946271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:36.516067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:42.252966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:47.574240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:53.877024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:59.480165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:05.564675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:41.957976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:47.701288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:53.184287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:59.094781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:04.530809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:10.549952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:16.247710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:22.093210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:27.856209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:33.689437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:39.227704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:45.174485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:50.752808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:56.747368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:02.289579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:08.467964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:14.032959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:19.925776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:25.134423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:31.167441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:36.750027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:42.448773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:47.792978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:54.084335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:59.695868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:05.760899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:42.156916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:47.903767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:53.395681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:59.292043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:04.724913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:10.751543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:16.469431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:22.284783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:28.088374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:33.911229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:39.454569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:45.359573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:50.952106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:56.945770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:02.489748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:08.679508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:14.221061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:20.127312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:25.327949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:31.389723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:36.962882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:42.640027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:48.000227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:54.291770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:59.909251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:05.977026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:42.377664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:48.118114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:53.615805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:59.513240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:04.957570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:10.981853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:16.711905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:22.494691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:28.330366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:34.153938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:39.704719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:45.573451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:51.180314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:57.160672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:02.714855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:08.915910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:14.433051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:20.348154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:25.548215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:31.630616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:37.183277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:42.854290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:48.241712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:54.513797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:00.146279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:06.173514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:42.585502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:48.318413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:53.823613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:21:59.736481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:05.173116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:11.191430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:16.940353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:22.695487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:28.546528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:34.359606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:39.944122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:45.769654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:51.396610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:22:57.370674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:02.917931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:09.153989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:14.628577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:20.548740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:25.751415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:31.846352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:37.387698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:43.069876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:48.454553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:23:54.723798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-16T09:24:00.366540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-01-16T09:24:38.594096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
agedelta_days_deathhospital_outcomeicuinpatient_dayslab_altlab_astlab_creatininelab_crplab_ddimerlab_glucoselab_hctlab_hemoglobinlab_inrlab_ldhlab_leukocytelab_lymphocytelab_lymphocyte_percentagelab_mchlab_mcvlab_neutrophillab_neutrophil_percentagelab_plateletlab_potassiumlab_rbclab_sodiumlab_ureanum_shotspmhx_activecancerpmhx_asthmapmhx_chfpmhx_chronicliverpmhx_ckdpmhx_copdpmhx_dementiapmhx_diabetespmhx_hldpmhx_htnpmhx_ihdpmhx_obesitypmhx_strokesextype_centervaccinatedwave_3wave_4wave_5wave_6wave_7
age1.000-0.1120.3570.2000.099-0.320-0.1410.3040.1010.3350.229-0.271-0.3570.225-0.0690.207-0.090-0.2240.0530.2430.2090.159-0.0220.210-0.3740.0460.5110.4140.1930.0590.4310.1110.3080.2780.3730.3070.3170.5330.2340.1020.1710.1280.0320.3740.1130.2710.2450.0900.311
delta_days_death-0.1121.0000.8450.1720.551-0.063-0.154-0.114-0.188-0.123-0.116-0.045-0.021-0.077-0.207-0.1180.1110.177-0.014-0.057-0.149-0.2070.006-0.053-0.024-0.068-0.1870.0590.0770.0420.1390.0230.0890.0720.1080.0320.0470.0410.0410.0180.0830.0530.0420.1910.1850.0880.0790.1210.190
hospital_outcome0.3570.8451.0000.1860.126-0.0890.0590.2280.1710.2380.157-0.124-0.1720.1600.1500.147-0.132-0.2090.0230.1310.1690.205-0.0430.139-0.1740.0470.3430.0990.0920.0490.1450.0040.1210.0800.1400.0950.0420.1630.0880.0390.0480.0080.0510.0920.0580.1230.0320.0490.035
icu0.2000.1720.1861.0000.3620.1060.1480.0240.1450.0070.0940.0610.068-0.0170.2040.033-0.090-0.1020.001-0.0310.0600.133-0.013-0.0140.067-0.0910.019-0.1230.0300.0180.0870.0220.0590.0470.0800.0000.0000.0310.0180.0590.0440.0740.0210.1130.0520.0390.0200.0200.103
inpatient_days0.0990.5510.1260.3621.0000.0110.0960.0830.1650.0750.116-0.017-0.0400.0480.1690.036-0.131-0.1450.0070.0240.0690.155-0.0560.022-0.043-0.1000.126-0.0560.0000.0000.0000.0090.0000.0000.0000.0230.0070.0190.0000.0210.0000.0200.0110.0260.0230.0000.0000.0150.037
lab_alt-0.320-0.063-0.0890.1060.0111.0000.764-0.0710.022-0.036-0.0080.2490.316-0.1020.3380.0070.0830.0570.054-0.0670.008-0.0090.072-0.0630.276-0.034-0.145-0.2280.0060.0000.0160.0000.0000.0110.0110.0000.0080.0080.0000.0210.0000.0020.0000.0000.0090.0000.0000.0160.000
lab_ast-0.141-0.1540.0590.1480.0960.7641.0000.0630.1610.0970.0040.1270.1620.0160.602-0.059-0.0250.0080.0600.004-0.0340.041-0.0660.0120.127-0.0870.000-0.1920.0100.0000.0130.0000.0050.0070.0140.0000.0020.0000.0000.0000.0050.0030.0080.0120.0000.0000.0050.0080.008
lab_creatinine0.304-0.1140.2280.0240.083-0.0710.0631.0000.1650.2760.222-0.127-0.1580.2170.0600.165-0.037-0.1610.0440.1570.1740.130-0.0930.311-0.180-0.0150.7240.1970.0480.0400.2100.0100.5070.0820.0930.1610.1050.2090.1420.0210.0610.0430.0140.1610.0280.1030.0300.0590.109
lab_crp0.101-0.1880.1710.1450.1650.0220.1610.1651.0000.2290.192-0.112-0.1120.2130.3680.297-0.164-0.3740.0110.0250.3620.4110.1260.034-0.123-0.1330.2000.0040.0400.0450.0160.0000.0320.0060.0000.0660.0560.0690.0000.0120.0090.0890.0370.0550.0600.0530.0210.0410.057
lab_ddimer0.335-0.1230.2380.0070.075-0.0360.0970.2760.2291.0000.198-0.204-0.2430.1960.2330.3070.009-0.2200.0360.1780.3110.1890.1220.183-0.2620.0920.4110.2360.0000.0080.0060.0070.0000.0080.0150.0000.0000.0030.0000.0060.0190.0140.0120.0100.0260.0160.0000.0130.008
lab_glucose0.229-0.1160.1570.0940.116-0.0080.0040.2220.1920.1981.000-0.091-0.0950.1390.0950.239-0.111-0.272-0.0430.0070.2800.2880.0670.126-0.072-0.1190.3030.1040.0370.0200.1030.0530.1080.0520.0890.4250.1270.1870.0930.0690.0590.0030.0380.1040.0270.0700.0340.0450.062
lab_hct-0.271-0.045-0.1240.061-0.0170.2490.127-0.127-0.112-0.204-0.0911.0000.867-0.3080.077-0.0110.0880.0460.074-0.003-0.022-0.036-0.072-0.0650.7950.040-0.219-0.2170.1630.0220.2270.0360.2600.0890.1290.1420.0920.1890.1160.0000.0640.2560.1000.2090.0710.1040.0370.0670.164
lab_hemoglobin-0.357-0.021-0.1720.068-0.0400.3160.162-0.158-0.112-0.243-0.0950.8671.000-0.2160.093-0.0370.1010.1070.204-0.035-0.044-0.075-0.081-0.1150.837-0.011-0.282-0.2780.1730.0230.2750.0290.2940.1020.1540.1740.1080.2290.1350.0120.0770.3130.0190.2500.0700.1350.0530.0810.195
lab_inr0.225-0.0770.160-0.0170.048-0.1020.0160.2170.2130.1960.139-0.308-0.2161.0000.1050.181-0.041-0.147-0.0250.0790.1940.1240.0410.093-0.2000.0280.2750.1870.0100.0090.1570.0000.0820.0530.0430.0500.0320.0870.0560.0140.0360.0050.0190.0600.0150.0330.0170.0120.042
lab_ldh-0.069-0.2070.1500.2040.1690.3380.6020.0600.3680.2330.0950.0770.0930.1051.0000.128-0.089-0.1880.019-0.0350.1780.2500.0610.1020.082-0.1060.082-0.1740.0230.0190.0160.0000.0160.0190.0000.0090.0000.0100.0000.0020.0190.0100.0300.0430.0320.0000.0000.0000.030
lab_leukocyte0.207-0.1180.1470.0330.0360.007-0.0590.1650.2970.3070.239-0.011-0.0370.1810.1281.0000.232-0.505-0.0330.0450.9550.4800.4470.123-0.0270.0280.3120.2150.0690.0000.0000.0000.0150.0000.0000.0030.0000.0100.0090.0000.0000.0120.0090.0250.0120.0000.0000.0230.013
lab_lymphocyte-0.0900.111-0.132-0.090-0.1310.083-0.025-0.037-0.1640.009-0.1110.0880.101-0.041-0.0890.2321.0000.625-0.041-0.0230.024-0.5870.2220.0070.1150.114-0.0840.0010.0670.0000.0000.0000.0130.0000.0000.0070.0010.0080.0120.0000.0000.0100.0090.0230.0100.0020.0000.0210.015
lab_lymphocyte_percentage-0.2240.177-0.209-0.102-0.1450.0570.008-0.161-0.374-0.220-0.2720.0460.107-0.147-0.188-0.5050.6251.000-0.004-0.044-0.674-0.933-0.150-0.0790.1090.085-0.304-0.1470.1550.0090.1050.0180.1020.0940.0700.0680.0640.1200.0520.0530.0330.0890.0540.1490.0350.1190.0510.0700.110
lab_mch0.053-0.0140.0230.0010.0070.0540.0600.0440.0110.036-0.0430.0740.204-0.0250.019-0.033-0.041-0.0041.0000.780-0.032-0.007-0.1720.007-0.268-0.0300.0290.0310.0190.0290.0410.0080.0160.0140.0220.0820.0260.0430.0200.0630.0010.1160.0390.0400.0280.0090.0000.0190.024
lab_mcv0.243-0.0570.131-0.0310.024-0.0670.0040.1570.0250.1780.007-0.003-0.0350.079-0.0350.045-0.023-0.0440.7801.0000.0350.011-0.1240.169-0.4210.1170.2050.1840.0760.0310.1580.0190.1390.1050.1210.0640.0440.1130.0660.0430.0390.0510.0200.1820.0410.1080.0400.0700.128
lab_neutrophil0.209-0.1490.1690.0600.0690.008-0.0340.1740.3620.3110.280-0.022-0.0440.1940.1780.9550.024-0.674-0.0320.0351.0000.6700.4200.114-0.0350.0030.3310.1940.0270.0000.0320.0060.0320.0360.0270.0180.0000.0280.0180.0180.0080.0080.0130.0640.0180.0400.0000.0290.045
lab_neutrophil_percentage0.159-0.2070.2050.1330.155-0.0090.0410.1300.4110.1890.288-0.036-0.0750.1240.2500.480-0.587-0.933-0.0070.0110.6701.0000.1390.052-0.073-0.0800.2700.0750.1550.0110.0700.0210.0750.0560.0480.0490.0490.0870.0350.0530.0220.0570.0580.1090.0420.0890.0360.0540.075
lab_platelet-0.0220.006-0.043-0.013-0.0560.072-0.066-0.0930.1260.1220.067-0.072-0.0810.0410.0610.4470.222-0.150-0.172-0.1240.4200.1391.0000.0400.0020.0460.0080.0070.0770.0300.0470.0530.0570.0280.0210.0300.0360.0330.0360.0290.0000.0980.0160.0570.0350.0500.0350.0440.033
lab_potassium0.210-0.0530.139-0.0140.022-0.0630.0120.3110.0340.1830.126-0.065-0.1150.0930.1020.1230.007-0.0790.0070.1690.1140.0520.0401.000-0.117-0.0210.3540.1310.0500.0240.1690.0000.2470.0920.0810.1560.0790.1570.1010.0140.0380.0730.0600.1170.0140.0950.0410.0550.075
lab_rbc-0.374-0.024-0.1740.067-0.0430.2760.127-0.180-0.123-0.262-0.0720.7950.837-0.2000.082-0.0270.1150.109-0.268-0.421-0.035-0.0730.002-0.1171.0000.006-0.293-0.2870.1770.0250.2560.0310.2960.1000.1580.1280.1040.2120.1270.0280.0680.2250.0330.2580.0710.1370.0560.0800.200
lab_sodium0.046-0.0680.047-0.091-0.100-0.034-0.087-0.015-0.1330.092-0.1190.040-0.0110.028-0.1060.0280.1140.085-0.0300.1170.003-0.0800.046-0.0210.0061.0000.0820.0920.0270.0160.0780.0270.0500.0280.1820.0560.0210.0680.0100.0280.0600.0660.0390.1170.0460.0660.0060.0220.103
lab_urea0.511-0.1870.3430.0190.126-0.1450.0000.7240.2000.4110.303-0.219-0.2820.2750.0820.312-0.084-0.3040.0290.2050.3310.2700.0080.354-0.2930.0821.0000.2760.0730.0470.2820.0000.4550.1210.1950.2190.1260.2900.1600.0000.0960.0030.0380.2140.0000.1540.0500.0720.152
num_shots0.4140.0590.099-0.123-0.056-0.228-0.1920.1970.0040.2360.104-0.217-0.2780.187-0.1740.2150.001-0.1470.0310.1840.1940.0750.0070.131-0.2870.0920.2761.0000.1720.0120.2990.0310.2580.2230.2400.1500.1230.2470.1420.0300.1270.0230.0351.0000.5270.3300.3230.4110.684
pmhx_activecancer0.1930.0770.0920.0300.0000.0060.0100.0480.0400.0000.0370.1630.1730.0100.0230.0690.0670.1550.0190.0760.0270.1550.0770.0500.1770.0270.0730.1721.0000.0130.0620.0370.0690.0970.0130.0600.0550.1040.0440.0130.0110.0300.0280.1520.0360.0760.0340.0520.102
pmhx_asthma0.0590.0420.0490.0180.0000.0000.0000.0400.0450.0080.0200.0220.0230.0090.0190.0000.0000.0090.0290.0310.0000.0110.0300.0240.0250.0160.0470.0120.0131.0000.0360.0170.0120.1840.0250.0100.0000.0000.0040.0810.0120.1080.0090.0100.0170.0130.0000.0130.021
pmhx_chf0.4310.1390.1450.0870.0000.0160.0130.2100.0160.0060.1030.2270.2750.1570.0160.0000.0000.1050.0410.1580.0320.0700.0470.1690.2560.0780.2820.2990.0620.0361.0000.0430.3490.2630.1770.2120.1590.3170.3090.1160.1310.0660.0080.2640.0600.1520.0580.0810.203
pmhx_chronicliver0.1110.0230.0040.0220.0090.0000.0000.0100.0000.0070.0530.0360.0290.0000.0000.0000.0000.0180.0080.0190.0060.0210.0530.0000.0310.0270.0000.0310.0370.0170.0431.0000.0430.0490.0040.1260.0840.0910.0250.1250.0000.0120.0190.0230.0050.0000.0250.0120.000
pmhx_ckd0.3080.0890.1210.0590.0000.0000.0050.5070.0320.0000.1080.2600.2940.0820.0160.0150.0130.1020.0160.1390.0320.0750.0570.2470.2960.0500.4550.2580.0690.0120.3490.0431.0000.1390.1230.2340.1680.2850.2150.0670.1050.0120.0140.2260.0540.1230.0430.0700.163
pmhx_copd0.2780.0720.0800.0470.0000.0110.0070.0820.0060.0080.0520.0890.1020.0530.0190.0000.0000.0940.0140.1050.0360.0560.0280.0920.1000.0280.1210.2230.0970.1840.2630.0490.1391.0000.0740.1210.0990.1780.1470.0740.0610.1310.0190.2030.0540.1020.0570.0500.174
pmhx_dementia0.3730.1080.1400.0800.0000.0110.0140.0930.0000.0150.0890.1290.1540.0430.0000.0000.0000.0700.0220.1210.0270.0480.0210.0810.1580.1820.1950.2400.0130.0250.1770.0040.1230.0741.0000.1120.0760.1610.0760.0400.1740.0650.0000.2140.0470.1300.0410.0500.180
pmhx_diabetes0.3070.0320.0950.0000.0230.0000.0000.1610.0660.0000.4250.1420.1740.0500.0090.0030.0070.0680.0820.0640.0180.0490.0300.1560.1280.0560.2190.1500.0600.0100.2120.1260.2340.1210.1121.0000.2530.3210.2020.1810.1040.0000.0210.1440.0180.0920.0590.0300.095
pmhx_hld0.3170.0470.0420.0000.0070.0080.0020.1050.0560.0000.1270.0920.1080.0320.0000.0000.0010.0640.0260.0440.0000.0490.0360.0790.1040.0210.1260.1230.0550.0000.1590.0840.1680.0990.0760.2531.0000.3040.1970.1040.0870.0280.0260.1140.0370.0490.0910.0050.081
pmhx_htn0.5330.0410.1630.0310.0190.0080.0000.2090.0690.0030.1870.1890.2290.0870.0100.0100.0080.1200.0430.1130.0280.0870.0330.1570.2120.0680.2900.2470.1040.0000.3170.0910.2850.1780.1610.3210.3041.0000.2240.1460.1320.0630.0200.2290.0330.1340.1250.0590.151
pmhx_ihd0.2340.0410.0880.0180.0000.0000.0000.1420.0000.0000.0930.1160.1350.0560.0000.0090.0120.0520.0200.0660.0180.0350.0360.1010.1270.0100.1600.1420.0440.0040.3090.0250.2150.1470.0760.2020.1970.2241.0000.0550.1030.0780.0000.1300.0120.0790.0360.0330.098
pmhx_obesity0.1020.0180.0390.0590.0210.0210.0000.0210.0120.0060.0690.0000.0120.0140.0020.0000.0000.0530.0630.0430.0180.0530.0290.0140.0280.0280.0000.0300.0130.0810.1160.1250.0670.0740.0400.1810.1040.1460.0551.0000.0000.0840.0250.0000.0000.0240.0040.0000.023
pmhx_stroke0.1710.0830.0480.0440.0000.0000.0050.0610.0090.0190.0590.0640.0770.0360.0190.0000.0000.0330.0010.0390.0080.0220.0000.0380.0680.0600.0960.1270.0110.0120.1310.0000.1050.0610.1740.1040.0870.1320.1030.0001.0000.0110.0130.1120.0250.0660.0250.0260.097
sex0.1280.0530.0080.0740.0200.0020.0030.0430.0890.0140.0030.2560.3130.0050.0100.0120.0100.0890.1160.0510.0080.0570.0980.0730.2250.0660.0030.0230.0300.1080.0660.0120.0120.1310.0650.0000.0280.0630.0780.0840.0111.0000.0000.0200.0030.0030.0070.0000.019
type_center0.0320.0420.0510.0210.0110.0000.0080.0140.0370.0120.0380.1000.0190.0190.0300.0090.0090.0540.0390.0200.0130.0580.0160.0600.0330.0390.0380.0350.0280.0090.0080.0190.0140.0190.0000.0210.0260.0200.0000.0250.0130.0001.0000.0450.1110.0710.0360.0170.040
vaccinated0.3740.1910.0920.1130.0260.0000.0120.1610.0550.0100.1040.2090.2500.0600.0430.0250.0230.1490.0400.1820.0640.1090.0570.1170.2580.1170.2141.0000.1520.0100.2640.0230.2260.2030.2140.1440.1140.2290.1300.0000.1120.0200.0451.0000.5260.2910.0680.3790.501
wave_30.1130.1850.0580.0520.0230.0090.0000.0280.0600.0260.0270.0710.0700.0150.0320.0120.0100.0350.0280.0410.0180.0420.0350.0140.0710.0460.0000.5270.0360.0170.0600.0050.0540.0540.0470.0180.0370.0330.0120.0000.0250.0030.1110.5261.0000.3300.2720.3280.308
wave_40.2710.0880.1230.0390.0000.0000.0000.1030.0530.0160.0700.1040.1350.0330.0000.0000.0020.1190.0090.1080.0400.0890.0500.0950.1370.0660.1540.3300.0760.0130.1520.0000.1230.1020.1300.0920.0490.1340.0790.0240.0660.0030.0710.2910.3301.0000.1920.2320.217
wave_50.2450.0790.0320.0200.0000.0000.0050.0300.0210.0000.0340.0370.0530.0170.0000.0000.0000.0510.0000.0400.0000.0360.0350.0410.0560.0060.0500.3230.0340.0000.0580.0250.0430.0570.0410.0590.0910.1250.0360.0040.0250.0070.0360.0680.2720.1921.0000.1900.179
wave_60.0900.1210.0490.0200.0150.0160.0080.0590.0410.0130.0450.0670.0810.0120.0000.0230.0210.0700.0190.0700.0290.0540.0440.0550.0800.0220.0720.4110.0520.0130.0810.0120.0700.0500.0500.0300.0050.0590.0330.0000.0260.0000.0170.3790.3280.2320.1901.0000.216
wave_70.3110.1900.0350.1030.0370.0000.0080.1090.0570.0080.0620.1640.1950.0420.0300.0130.0150.1100.0240.1280.0450.0750.0330.0750.2000.1030.1520.6840.1020.0210.2030.0000.1630.1740.1800.0950.0810.1510.0980.0230.0970.0190.0400.5010.3080.2170.1790.2161.000

Missing values

2024-01-16T09:24:06.643133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-16T09:24:07.399335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-16T09:24:08.217651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sexagenum_shotstype_centervaccinatedicuinpatient_daysadmission_datetimedischarge_datetimehospital_outcomedeath_datetimedelta_days_deathwave_3wave_4wave_5wave_6wave_7pmhx_activecancerpmhx_asthmapmhx_chfpmhx_chronicliverpmhx_ckdpmhx_copdpmhx_dementiapmhx_diabetespmhx_hldpmhx_htnpmhx_ihdpmhx_obesitypmhx_strokelab_altlab_astlab_creatininelab_crplab_ddimerlab_glucoselab_hctlab_hemoglobinlab_inrlab_ldhlab_leukocytelab_lymphocytelab_lymphocyte_percentagelab_mchlab_mcvlab_neutrophillab_neutrophil_percentagelab_plateletlab_potassiumlab_rbclab_sodiumlab_urea
00260County Hospital01152021-07-292021-08-130NaT<NA>001000000010001010237.00262.280.8376.30950.00145.0042.2014.801.03856.004.751.0822.729.8084.903.2969.20143.004.504.98136.0032.00
10502County Hospital1062022-01-012022-01-070NaT<NA>00010000000001000039.7646.061.19101.051050.00140.1538.9813.541.25359.208.211.3516.429.6189.416.2275.45219.314.184.59136.9850.64
20220County Hospital0062021-02-032021-02-090NaT<NA>100000000000000010168.00130.930.8630.50800.00103.0044.2016.300.87313.0010.841.0910.128.1076.209.0483.40248.003.805.79133.0033.00
31410County Hospital00102021-05-292021-06-080NaT<NA>01000000000000000045.1251.210.58215.60890.00118.0044.4014.700.97448.0612.780.453.528.8086.8011.9693.60220.003.905.11134.0020.00
40430County Hospital00112021-05-292021-06-090NaT<NA>01000000000000000069.0050.970.99224.50340.00118.0044.7015.101.14271.0010.000.585.830.2089.509.1191.10229.003.905.00131.0036.64
50783County Hospital10142022-11-142022-11-280NaT<NA>00001111001001101019.004.480.83137.301737.75136.0043.2014.200.96134.0012.052.6622.132.1097.908.4570.10115.003.604.42137.0041.00
61560County Hospital01532021-07-062021-08-2812021-08-285300100000001000100034.0044.350.7852.00910.00178.0039.0013.100.96389.008.740.829.428.7085.707.4284.90209.003.204.55144.0044.00
71410County Hospital0042021-04-082021-04-120NaT<NA>01000000000000100010.006.010.5239.201500.00101.0045.3015.300.93211.002.580.9637.428.7085.101.2649.00196.003.705.31137.0012.00
80520County Hospital01422021-04-142021-05-260NaT<NA>010000000000110010141.00158.990.9677.80340.00178.0045.9015.600.95640.006.981.3919.929.0085.405.1173.20188.003.805.37132.0026.00
110812County Hospital1072021-10-222021-10-290NaT<NA>00010000001011000021.0022.630.72166.301910.00121.0040.6013.301.15313.006.801.1516.930.5092.905.0273.80322.003.704.37134.0019.00
sexagenum_shotstype_centervaccinatedicuinpatient_daysadmission_datetimedischarge_datetimehospital_outcomedeath_datetimedelta_days_deathwave_3wave_4wave_5wave_6wave_7pmhx_activecancerpmhx_asthmapmhx_chfpmhx_chronicliverpmhx_ckdpmhx_copdpmhx_dementiapmhx_diabetespmhx_hldpmhx_htnpmhx_ihdpmhx_obesitypmhx_strokelab_altlab_astlab_creatininelab_crplab_ddimerlab_glucoselab_hctlab_hemoglobinlab_inrlab_ldhlab_leukocytelab_lymphocytelab_lymphocyte_percentagelab_mchlab_mcvlab_neutrophillab_neutrophil_percentagelab_plateletlab_potassiumlab_rbclab_sodiumlab_urea
499520670County Hospital0042021-01-272021-01-310NaT<NA>10000000100111100033.0027.000.7581.002233.00237.0037.2012.101.13243.009.671.4615.1028.9089.007.2374.80309.05.004.18137.021.00
499551872County Hospital1032021-12-292022-01-0112022-01-01300010000000111100040.9751.441.07122.16410.86161.3338.5413.551.19405.568.071.3515.1129.7186.286.0276.58210.04.254.58121.028.30
499561600County Hospital00352021-02-262021-04-0212021-04-023510000000000011100025.0077.001.21260.605222.00193.0039.2012.701.561270.007.130.9713.7028.2087.105.8281.70289.04.604.50129.043.00
499580623County Hospital1072022-08-122022-08-190NaT<NA>00001010001011001016.0028.000.88310.40893.00148.0043.5014.001.24499.9016.331.358.3028.2087.5013.7884.40354.05.204.97133.020.00
499590462County Hospital1042022-01-062022-01-100NaT<NA>00010100000001101032.0023.000.88194.40882.00110.0035.4012.101.21310.009.551.1512.1031.7092.707.3276.70297.04.103.82135.026.00
499601320County Hospital0052021-05-062021-05-110NaT<NA>01000010000000001020.0024.000.6134.20424.0096.0037.3012.101.16294.008.861.6618.8027.6085.206.2770.80319.04.304.38139.021.00
499611770County Hospital00102020-12-292021-01-080NaT<NA>10000000000011100013.0029.000.92183.801116.0098.0046.4014.901.12360.003.240.8927.5031.0096.702.2068.20162.04.104.80134.023.00
499651853County Hospital1062022-05-182022-05-240NaT<NA>00001001010111101011.0014.001.49193.601059.00226.0040.2012.301.32153.0018.141.458.0030.80100.8015.6586.30480.04.303.99135.076.29
499661840County Hospital0042021-01-162021-01-200NaT<NA>10000000000001100016.0030.000.88171.401109.00118.0036.9012.101.33433.006.780.9614.3030.3092.504.8471.40179.03.703.99136.035.00
499681883County Hospital1072022-06-252022-07-020NaT<NA>00001010001001100031.0019.000.8768.80791.00113.0034.9011.601.21180.007.180.7410.4032.3097.205.8080.90186.03.503.59133.032.00